Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have been applied to many different fields and problems including, but limited to, image processing since 1988. However, most of the prior art CNN approaches are either based on software solutions (e.g., Convolutional Neural Networks, Recurrent Neural Networks, etc.) or based on hardware that are designed for other purposes (e.g., graphic processing, general computation, etc.). As a result, CNN prior approaches are too slow in term of computational speed and/or too expensive thereby impractical for processing large amount of imagery data. Imagery data can be from any two-dimensional data (e.g., still photo, picture, a frame of a video stream, converted form of voice data, etc.).
Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches which try to learn one hypothesis from trained data, ensemble methods try to construct a set of hypotheses and combine them to use. Ensemble includes a number of base learners each using a different hypothesis. Then a meta learner uses for combining the results (e.g., features) obtained from the base learners.